Integrating Artificial Intelligence and Remote Sensing for Wildfire Detection, Monitoring and Management
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 5375
Editor
Interests: artificial intelligence; wildfire; bushfire risk modelling and reduction; earth and space science informatics; environmental assessment and monitoring; photogrammetry and remote sensing; natural hazards; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Wildfires are increasingly impacting ecosystems, biodiversity, and human livelihoods across the globe, driven by climate change, land use practices, and human activity. As the frequency and intensity of wildfires continue to rise, there is an urgent need for advanced tools and technologies to enhance fire detection and monitoring, improve risk assessment, and strengthen preparedness, response, and recovery efforts.
This Special Issue invites manuscripts that highlight the transformative potential of integrating artificial intelligence (AI) and remote sensing technologies in wildfire science and management. By combining multi-source Earth observation products—from satellites, aircraft, and ground-based platforms—with advanced AI techniques such as machine learning, deep learning, and data fusion, researchers and practitioners can significantly enhance the speed, accuracy, and scalability of fire-related insights. These innovations contribute to a deeper understanding of the complex interactions between wildfires, ecosystems, and society, and support the development of actionable strategies to improve wildfire management, ecosystem resilience, and multifunctionality.
Specific topics of interests include, but are not limited to, the following areas:
- AI-driven wildfire detection and fire-prone landscape analysis for hazard potential forecasting.
- Advanced machine learning techniques for fire-weather prediction using remote sensing data.
- AI and ML-driven integration of climate and fuel data for operational wildfire forecasting.
- AI-enabled decision support systems integrating remote sensing, meteorological forecasts, and historical fire data for optimized wildfire management and response planning.
- AI-enhanced remote sensing-based evaluation of wildfire impacts and landscape recovery.
- AI-based remote sensing assessment of fire-induced changes in ecosystem functioning and services.
- AI-integrated remote sensing for modelling and mapping biophysical fuel characteristics.
- AI-powered remote sensing for wildfire risk and flammability analysis and monitoring.
- AI-enabled remote sensing evaluation of wildfire emissions and their impacts on climate and health.
Dr. Arnick Abdollahi
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- wildfire
- bushfire risk modelling and reduction
- earth and space science informatics
- environmental assessment and monitoring
- photogrammetry and remote sensing
- natural hazards
- image processing
- machine learning
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